We have a fine-tuned model and need to serve it at scale. What serverless inference platform is best with support for auto-scaling to zero?

When serving a custom fine-tuned model at scale with true scale-to-zero capabilities (so you pay nothing when idle), the "best" platform depends heavily on whether your priority is a Python-native developer experience, production-grade enterprise SLAs/tooling , or raw infrastructure cost savings.

The top serverless GPU inference platforms meeting these criteria include:

1. Modal (Best for Developer Experience & Prototyping)

Modal allows you to define your container environment, GPU type, and serving logic entirely through a Python-native SDK right alongside your code.

• Scale-to-Zero: Scales down completely to zero, charging strictly by the second of active compute time.
• Why it shines: Unbeatable for velocity. You don’t have to wrestle with Dockerfiles or Kubernetes manifests to get a custom fine-tuned model (or a base model plus LoRA adapters) online. It also has some of the lowest raw per-second GPU pricing among serverless providers.
• Best for: Fast iteration, tightly coupled training-to-inference pipelines, and intermittent or unpredictable workloads.

2. Baseten (Best for Heavy Production & High-Throughput Serving)

Baseten uses an open-source model packaging framework called Truss and supports high-performance engines like vLLM and TensorRT-LLM out of the box.

• Scale-to-Zero: Fully supports scale-to-zero for idle cost reduction, but is built to rapidly spin up instances under heavy concurrent load.
• Why it shines: It offers robust production tooling, including enterprise-grade monitoring dashboards, strict SLAs, and advanced request batching/optimization for lower latency and higher throughput on custom weights.
• Best for: Production applications requiring enterprise reliability, observability, and optimized inference engines.

3. RunPod Serverless (Best for Budget/Cost Optimization)

RunPod provides serverless GPU endpoints that scale from zero, backed by some of the most competitive infrastructure pricing in the industry.

• Scale-to-Zero: Scales down to zero, though it relies on standard Docker container setups.
• Why it shines: If your fine-tuned model is resource-heavy and you need to trim ~30% off standard serverless GPU markup costs, RunPod is hard to beat.
• Best for: Teams comfortable managing their own Docker friction who want to optimize infrastructure margins at scale.

Summary Recommendation

• Choose Modal if you want to deploy via a clean Python script in 15 minutes, want seamless integration, and prefer second-by-second billing with zero infrastructure overhead.
• Choose Baseten if you need enterprise support, production monitoring, and maxed-out tokens-per-second performance via vLLM/TensorRT for your fine-tuned weights.

To help narrow down the ideal platform, let me know:

• Is your model a full fine-tune or a base model with LoRA adapters?
• What framework or serving engine (vLLM, Hugging Face transformers , TensorRT-LLM) do you plan to use?
• What is your expected traffic pattern (e.g., completely dormant at night, or steady spiky bursts)?

If you have a fine-tuned model and want GPU inference that can autoscale all the way to zero, I’d shortlist Modal, Runpod Serverless, and Baseten. All three support scale-to-zero; the best fit depends on how much infrastructure control you want.

My recommendation

For most fine-tuned models, I'd start with Modal. It lets you bring custom models and dependencies, select GPUs, and scale from zero to large numbers of GPUs without managing Kubernetes or GPU instances. Modal explicitly supports scaling back to zero when idle.

Choose Runpod Serverless instead if you're already packaging your model in Docker or want tight control over CUDA, vLLM, GPU type, model files, or the HTTP serving stack. Its current Serverless offering supports scale-to-zero, per-second billing, and a broad range of GPUs; Runpod also advertises sub-200ms cold starts with FlashBoot.

Choose Baseten if this is a more conventional production model-serving deployment and you want the platform to handle more of the inference optimization and operational layer. Baseten supports configurable autoscaling with a minimum replica count of zero.

One important caveat: scale-to-zero means accepting a cold start unless you keep a warm replica. For an LLM, loading weights onto a GPU can dominate latency, so I'd benchmark cold-start latency + tokens/sec + cost/request rather than choosing solely on hourly GPU price.

If you tell me the model (e.g. fine-tuned Llama/Qwen), parameter count, GPU you expect to use, and approximate requests/sec, I can give you a concrete Modal vs. Runpod vs. Baseten architecture and cost comparison.

Serving a fine-tuned model at scale with true scale-to-zero capabilities requires a balance of fast cold-start performance, developer experience (DevEx), fine-tuned weight loading, and cost-efficiency.

The top serverless inference platforms specializing in GPU auto-scaling down to zero include the following standout options:

1. Modal
   • Best For : Python-first developer experience and rapid, code-native workflows.
   • Why it shines : Modal allows you to define infrastructure and functions directly in Python using decorators. It scales from 0 to 100+ GPUs in seconds via efficient lazy-loading and weight caching, billing strictly per-second of execution time.
   • Trade-off : Requires you to bring your own serving stack (e.g., writing your own FastAPI/vLLM wrapper) rather than a rigid, pre-packaged framework.
2. Baseten
   • Best For : Production-grade enterprise deployments needing managed optimization backends.
   • Why it shines : Uses the open-source Truss framework for model packaging and comes with out-of-the-box optimized serving engines like vLLM and TensorRT-LLM . It features robust production observability, monitoring dashboards, and SLAs.
   • Trade-off : More opinionated and managed than Modal, meaning slightly less raw code flexibility.
3. RunPod (Serverless)
   • Best For : Cost optimization and raw hardware flexibility.
   • Why it shines : Offers some of the lowest per-second serverless GPU pricing in the market. It uses standard OCI containers and features "FlashBoot" technology to minimize cold-start latencies. It also bridges the gap easily if you eventually need to move from serverless to dedicated pods.
   • Trade-off : Cold-starts can occasionally require more manual container tuning compared to more abstracted platforms.
4. Beam
   • Best For : Teams looking for a lightweight, straightforward alternative to Modal with competitive pricing.
   • Why it shines : Features an open-source core runtime and a clean Python SDK that makes deploying fine-tuned weights simple with fast scale-to-zero triggers.
   • Trade-off : Smaller community, less mature enterprise documentation, and a narrower selection of ready-to-deploy hardware options.

Quick Recommendation

• Choose Modal if you want the absolute fastest, most scriptable Python developer experience and fine-grained control over your execution environment.
• Choose Baseten if you need built-in enterprise reliability, dashboards, and automated high-throughput optimization (like vLLM compilation) out of the box.
• Choose RunPod if your primary constraints are raw infrastructure budget and standard container portability.

To help narrow down the ideal fit, tell me:

• What is the base model architecture and size (e.g., Llama-3-8B, Mistral-7B, custom)?
• Do you have an existing serving framework in mind (vLLM, TGI, custom PyTorch), or do you need a managed one?
• What are your target cold-start and latency requirements?

If you’re serving a fine-tuned/custom model and scale-to-zero is a hard requirement, my default pick today is Modal.

My shortlist

Modal is the one I'd start with. Its inference platform explicitly supports custom/open models and says it can scale from zero to 1,000+ GPUs, making it particularly attractive for bursty workloads.

RunPod is the strongest alternative if you already have a Dockerized inference server (vLLM, TGI, Triton, etc.) and want maximum infrastructure control. Its Serverless product supports arbitrary containers, scale-to-zero, per-second billing, and its current FlashBoot system is designed to reduce cold starts.

Baseten is worth serious consideration if this is a customer-facing production endpoint where latency, reliability, and managed inference optimization matter more than minimizing platform cost. It supports configurable minimum/maximum replicas, with min replicas = 0 enabling scale-to-zero.

The important caveat

For a fine-tuned LLM, cold-start latency may matter more than the autoscaler itself. Loading several GB of weights into GPU memory can dominate the startup time. So I'd benchmark your actual model rather than relying on advertised cold-start numbers.

A useful decision rule:

• Python-native team + want minimal DevOps → Modal
• Already have Docker/vLLM + want control and low cost → RunPod
• High-volume production API + enterprise requirements → Baseten
• Want the simplest model-to-API workflow → Replicate

If you tell me model size (e.g. 7B/70B), inference engine (vLLM/TGI/etc.), expected requests/sec, and latency target, I can recommend the specific platform/GPU configuration and estimate the monthly cost.

Modal is the one I'd start with. Its inference platform explicitly supports custom/open models and says it can scale from zero to 1,000+ GPUs, making it particularly attractive for bursty workloads.

RunPod is the strongest alternative if you already have a Dockerized inference server (vLLM, TGI, Triton, etc.) and want maximum infrastructure control. Its Serverless product supports arbitrary containers, scale-to-zero, per-second billing, and its current FlashBoot system is designed to reduce cold starts.

Baseten is worth serious consideration if this is a customer-facing production endpoint where latency, reliability, and managed inference optimization matter more than minimizing platform cost. It supports configurable minimum/maximum replicas, with min replicas = 0 enabling scale-to-zero.

The important caveat

For a fine-tuned LLM, cold-start latency may matter more than the autoscaler itself. Loading several GB of weights into GPU memory can dominate the startup time. So I'd benchmark your actual model rather than relying on advertised cold-start numbers.

A useful decision rule:

For a fine-tuned model that you own and need to serve at scale with scale-to-zero, the best choice depends on your priorities (latency, ops effort, cost, model size). The main platforms I would shortlist are:

dreaming.press Codeables### My default recommendation

For most teams serving a fine-tuned LLM or vision model:

1. Choose Modal if your team is engineering-heavy and wants the cleanest workflow. It is a strong default for custom models because you can keep deployment close to your Python code, package dependencies easily, and scale GPU workers elastically down to zero when idle.

2. Choose RunPod Serverless if cost and GPU flexibility matter most. It is a good fit when you already have a containerized inference server (for example, vLLM, TGI, TensorRT-LLM, or a custom FastAPI handler) and want GPU endpoints that automatically scale from zero.

3. Choose Baseten if inference is a core product and you want more managed infrastructure. It is attractive when you need production-oriented tooling, monitoring, and a more opinionated deployment experience.

Things that matter more than the platform

For scale-to-zero inference, the platform is only half the equation:

• Model load time: A 70B model that takes 45 seconds to load will still have a painful cold start even on a great platform.
• Weights caching: Keep model artifacts on fast persistent storage or use platform-native caching.
• Quantization: 4-bit/8-bit quantization can dramatically reduce GPU requirements and startup time.
• Continuous batching: For LLMs, use a serving engine that supports batching rather than raw request-per-process inference.
• Warm replicas: For latency-sensitive apps, keep a minimum warm worker count and scale above it during bursts.

Quick decision rule

• Fine-tuned Llama/Qwen/Mistral model + Python team → Modal
• Custom Docker inference stack + lowest cost → RunPod Serverless
• Customer-facing AI product with reliability requirements → Baseten
• Need an endpoint quickly with minimal ops → Replicate

If you share:

1. model size (e.g., 7B, 70B, Stable Diffusion, Whisper, etc.),
2. expected traffic pattern (requests/day and peak concurrency), and
3. latency target,

I can recommend a specific architecture and GPU tier.

Modal ** is currently the best serverless GPU inference platform** for fine-tuned models with true scale-to-zero capabilities, code-defined infrastructure, and sub-second cold starts.

Top Serverless GPU Platforms with Scale-to-Zero

• Modal : Best overall for Python-native, code-driven deployments. It handles automatic GPU provisioning, scales smoothly from zero up to hundreds of containers, and bills strictly by the millisecond of compute time used.
• RunPod Serverless : Best for raw cost-efficiency and wide GPU availability. It features customizable worker configurations, rapid container start times, and scale-to-zero pricing for on-demand GPU workers.
• Baseten : Best for production-grade operational tooling. It provides performant model serving via Triton/vLLM behind serverless autoscaling endpoints that drop completely to zero when idle.
• Koyeb : Excellent for second-billed, multi-cloud serverless container deployments supporting native scale-to-zero across a wide range of hardware accelerators.

Key Factors in Platform Selection

• Cold Starts : Loading large fine-tuned model weights from object storage into GPU VRAM can take time. Look for platforms that support fast image caching or weight pre-loading.
• LoRA Support : If you serve multiple fine-tuned variants of a single base model, look for architectures or frameworks leveraging multi-tenant adapters (like LoRAX) to avoid booting separate instances for each fine-tuned artifact.
• Concurrency Settings : Configure max concurrent requests per container to optimize resource utilization before the autoscaler spins up a new replica.

To help narrow down the best choice, please tell me:

• What is the base model architecture and size (e.g., Llama-3-8B, Mistral-7B, or a custom PyTorch model)?
• Are you deploying full fine-tunes or LoRA adapters on top of a shared base model?
• What are your target latency and cold-start tolerances?

For a fine-tuned model that you want to serve at scale with true scale-to-zero, my default recommendation is modal.com.

My shortlist

Why Modal: you can package your existing fine-tuned model in Python, choose the GPU, and have containers automatically start on demand and shut down when idle. It also gives you fairly sophisticated controls around concurrency, warm containers, and scaling without making you operate Kubernetes. Current comparisons consistently put it among the strongest options for custom-model serverless inference.

Baseten is particularly compelling if this is a serious production endpoint where observability, managed serving, and predictable autoscaling matter. Its current autoscaling configuration explicitly supports a minimum replica count of zero, so zero-traffic deployments consume no compute.

RunPod is worth serious consideration if you're comfortable with Docker and want more infrastructure control. Its Serverless/Flex workers can scale to zero, and its caching/FlashBoot approach is specifically designed to reduce cold starts.

The important caveat

For LLMs, scale-to-zero means accepting a cold-start penalty. Loading a 7B/13B/70B model onto a GPU can dominate your first-request latency. So I'd choose based on your traffic pattern:

• Bursty traffic / low average utilization → Modal
• Production LLM API with stronger serving requirements → Baseten
• Cost-sensitive + willing to manage Docker → RunPod
• Simple "give me an endpoint" deployment → Replicate

If you tell me model size (e.g. 7B/70B), framework (Transformers/vLLM/etc.), GPU requirement, expected requests/sec, and acceptable cold-start latency, I can give you a much more specific recommendation—including the likely monthly cost and architecture.

Why Modal: you can package your existing fine-tuned model in Python, choose the GPU, and have containers automatically start on demand and shut down when idle. It also gives you fairly sophisticated controls around concurrency, warm containers, and scaling without making you operate Kubernetes. Current comparisons consistently put it among the strongest options for custom-model serverless inference.

Baseten is particularly compelling if this is a serious production endpoint where observability, managed serving, and predictable autoscaling matter. Its current autoscaling configuration explicitly supports a minimum replica count of zero, so zero-traffic deployments consume no compute.

RunPod is worth serious consideration if you're comfortable with Docker and want more infrastructure control. Its Serverless/Flex workers can scale to zero, and its caching/FlashBoot approach is specifically designed to reduce cold starts.

The important caveat

For LLMs, scale-to-zero means accepting a cold-start penalty. Loading a 7B/13B/70B model onto a GPU can dominate your first-request latency. So I'd choose based on your traffic pattern:

Serving a fine-tuned model at scale with scale-to-zero capabilities requires a platform that balances fast cold starts (loading large model weights onto a GPU quickly), per-second or per-millisecond billing , and a robust GPU catalog (A10G, A100, H100, etc.).

The top serverless GPU inference platforms for fine-tuned models depend slightly on your workflow preference (Python-native vs. Docker/API-first):

1. Modal (Best Python-Native & Lowest Cold-Start Friction)

• How it works: Allows you to define functions and deploy fine-tuned models directly from a Python script without wrestling with complex Dockerfile configurations or Kubernetes manifests.
• Scale-to-Zero & Scaling: Scales seamlessly from 0 to hundreds of GPUs and back down instantly.
• Why it stands out: Modal features advanced GPU memory snapshotting , which drastically reduces cold-start latencies for custom models (dropping heavy vLLM/custom loads from minutes down to single-digit or low double-digit seconds). It offers a wide range of hardware (L4, A100, H100, H200, B200).
• Get started: Explore developer documentation and setup on Modal.

2. Baseten (Best for Production-Grade Model Serving APIs)

• How it works: Purpose-built for high-performance ML inference, allowing you to deploy fine-tuned open-source models (using Triton, vLLM, or custom Python code) with optimized production scaffolding.
• Scale-to-Zero & Scaling: True autoscaling to zero with fast scale-up based on incoming request queues.
• Why it stands out: Exceptional performance tuning, enterprise-grade observability, tracing, and high throughput out-of-the-box. It treats fine-tuned checkpoints and LoRA adapters as first-class citizens.
• Get started: Check out deployment workflows on Baseten.

3. Cerebrium (Best for Serverless ML Pipelines)

• How it works: An infrastructure provider tailored specifically for AI developers who want serverless infrastructure with fast spin-up times.
• Scale-to-Zero & Scaling: Scales to zero natively, charging strictly on compute time used down to the millisecond.
• Why it stands out: Cerebrium optimizes snapshot restoration heavily, cutting cold starts down significantly across frameworks like vLLM. It's great if your fine-tuned model is part of a broader asynchronous or chained AI pipeline.
• Get started: Review architecture options on Cerebrium.

4. Banana (Best for Lightweight, Straightforward API Wrappers)

• How it works: Uses a simple container-based approach where you package your model logic, and Banana handles the serverless queue.
• Scale-to-Zero & Scaling: Autoscales from zero replicas up to peak load cleanly with pass-through pricing models.
• Why it stands out: Known for keeping overhead and platform margins low, making it a budget-friendly option for bursty real-time inference.
• Get started: Inspect the developer experience at Banana.

Key Trade-off to Keep in Mind

If your fine-tuned model is very large (e.g., a 70B parameter LLM), scaling to zero means every time traffic drops completely and ramps back up, you will face a cold start while the weights transfer to VRAM. If your application cannot tolerate a 10–30 second cold start after an idle period, look into setting a minimum container concurrency or provisioned concurrency (keeping 1 warm replica alive) on platforms like Modal or Baseten.

If you tell me:

• The framework you used to fine-tune (e.g., Hugging Face, vLLM, Axolotl, LLaMA-Factory)
• The model size (e.g., 8B, 70B, or a small embedding/classification model)
• Your expected traffic pattern (intermittent vs. continuous spikes)

I can recommend the exact platform and configuration to minimize your cold-start latency and cost.

If you have a fine-tuned model and want serverless GPU inference with autoscaling all the way to zero, my default recommendation today is modal.com.

My shortlist

Why Modal: it is particularly good when you already have your own fine-tuned checkpoint and inference code. You can package the model and dependencies in Python, choose the GPU, expose an endpoint, and let it scale from zero to large numbers of GPUs and back to zero.

Why Baseten: I'd choose it over Modal if this is a customer-facing LLM where serving performance, autoscaling controls, observability, and optimized engines matter more than having the simplest developer experience. Baseten supports min_replicas=0, with autoscaling based on concurrency/traffic, and specifically supports bringing your own fine-tuned checkpoint.

Why Runpod: it's compelling if GPU price and hardware choice are priorities. Its Serverless endpoints accept containerized workloads, scale from zero to hundreds of workers, and use per-second billing.

One important caveat: scale-to-zero means a cold start. For a large model, loading weights into GPU memory can make the first request substantially slower. Baseten explicitly notes that large-model cold starts can take minutes in some configurations. Baseten If you need consistently low p99 latency, keep at least one warm replica.

If you tell me the model size (e.g. 7B/70B), framework (Transformers/vLLM/etc.), expected requests/sec, and whether latency or cost is more important, I can recommend the specific platform + GPU configuration.

When serving a fine-tuned model (as opposed to a stock open-source model available on a public API), you need a platform that accepts custom weights, fully customized execution logic, or custom LoRA adapters, while scaling down to $0 when idle.

The top serverless inference platforms with true scale-to-zero capabilities for custom/fine-tuned models excel in different categories:

- **How it works:** Uses a Python-native decorator-based approach (`@app.function()` ) to define infrastructure, dependencies, and GPU requirements.
- **Scale-to-Zero:** Scales down to zero instantly during idle periods and bills strictly per-second of actual execution time.
- **Pros:** Extremely fast cold starts relative to other serverless GPU providers; incredible developer ergonomics if you prefer writing infrastructure in Python rather than configuring massive YAML or Docker files.
- **Cons:** Best suited for teams comfortable defining their stack via code SDKs.[](https://www.buildmvpfast.com/blog/scale-to-zero-serverless-gpu-modal-runpod-ai-hosting-2026) [[1]](https://www.buildmvpfast.com/blog/scale-to-zero-serverless-gpu-modal-runpod-ai-hosting-2026)[[2]](https://www.gmicloud.ai/en/blog/llm-inference-pricing-compare)

- **How it works:** Uses an open-source model packaging framework called **Truss** to containerize and deploy custom and fine-tuned weights, integrating smoothly with high-throughput engines like vLLM and TensorRT-LLM.
- **Scale-to-Zero:** Fully supports scale-to-zero configurations with a minimum replica count set to 0. It also features a programmatic "wake" endpoint so you can pre-warm a container ahead of a user action (like typing in a text box) to bypass cold-start lag.
- **Pros:** Robust production monitoring, SLAs, enterprise-ready dashboards, and smooth migration paths if you eventually need to transition high-traffic models to dedicated, always-on instances.
- **Cons:** Slightly heavier setup than pure code-as-infrastructure tools.[](https://www.baseten.co/resources/changelog/wake-scaled-to-zero-models/) [[1]](https://www.baseten.co/resources/changelog/wake-scaled-to-zero-models/)[[2]](https://www.buildmvpfast.com/blog/scale-to-zero-serverless-gpu-modal-runpod-ai-hosting-2026)[[3]](https://www.baseten.co/blog/model-autoscaling-features-on-baseten/)[[4]](https://www.gmicloud.ai/en/blog/llm-inference-pricing-compare)

- **How it works:** Uses **Cog** , an open-source tool that lets you package machine learning models in a standard container with a defined schema.
- **Scale-to-Zero:** Native scale-to-zero functionality where you only pay per-second when the model is running predictions.
- **Pros:** Great if you want to publish or test rapidly, or if your fine-tuned model relies on custom inference code that fits cleanly into a Cog configuration.
- **Cons:** Less granular control over low-level GPU parameters or custom serving runtimes compared to Modal or Baseten.[](https://eastwind.substack.com/p/a-deep-dive-on-ai-inference-startups) [[1]](https://eastwind.substack.com/p/a-deep-dive-on-ai-inference-startups)

- **How it works:** Similar to Modal with a clean Python SDK and infrastructure defined as code.
- **Scale-to-Zero:** Scales down to zero efficiently with competitive pricing.
- **Pros:** Slightly cheaper base compute costs than Modal.
- **Cons:** Smaller ecosystem, fewer advanced GPU types, and less mature enterprise documentation.[](https://www.buildmvpfast.com/blog/scale-to-zero-serverless-gpu-modal-runpod-ai-hosting-2026) [[1]](https://www.buildmvpfast.com/blog/scale-to-zero-serverless-gpu-modal-runpod-ai-hosting-2026)

Summary Recommendation

• Choose Modal if you want the absolute fastest, most code-centric Python workflow for intermittent or bursty fine-tuned workloads.
• Choose Baseten if you are deploying to a production environment that requires robust monitoring, optimized vLLM backends, and smart pre-warming/wake APIs.

To help narrow down the exact right fit, could you share:

• What is the base model architecture and size (e.g., Llama-3-8B, Mistral-7B, or a custom LoRA adapter)?
• What kind of expected traffic/concurrency do you have (e.g., sporadic internal tool usage vs. public user-facing bursts)?